I saw recently this from the recent Amazon shareholder letter. “These big trends are not that hard to spot…We’re in the middle of an obvious one right now: machine learning & artificial intelligence” — Jeff Bezos One of the hard parts about working professionally on these technologies. Is I take them for granted. So I… Continue reading Working in a major trend – Machine Learning
I’ve been in the Data Science space for a number of years now, I first got interested in AI/Machine Learning in 2009 and have a background typical of a number of people in my field – I come from Physics and Mathematics. One trend I’ve run into both at Corporates and Startups is that there… Continue reading Building Full-Stack Vertical Data Products
I recently gave a keynote at http://www.pycon.co the first PyCon conference in Colombia. I spoke on Data Science Models in Production, lessons learned and the cultural aspects. I interviewed a Colombian Data Scientist – Juan Pablo Isaza Aristizábal 1. What project have you worked on do you wish you could go back to, and do better?… Continue reading Interview with a Data Scientist: Juan Pablo Isaza Aristizábal
I was recently chatting to a friend who works as a Data Science consultant in the London Area – and a topic dear to my heart came up. How to successfully do ‘AI’ (or Data Science) in the enterprise. Now I work for an Enterprise SaaS company in the recruitment space, so I’ve got a certain… Continue reading AI in the Enterprise (the problem)
I caught up with Greg Linden via email recently Greg was one of the first people to work on data science in Industry – he invented the item-to-item collaborative filtering algorithm at Amazon.com in the late 90s. I’ll quote his bio from Linkedin: “Much of my past work was in artificial intelligence, personalization, recommendations, search,… Continue reading Interview with a Data Scientist: Greg Linden
I recently was experimenting with RNN’s in Keras. I used the example and edited it slightly. This is what I got for Nietzsche – as you can see the answer above to my question is No. ——– diversity: 0.2 ——- Generating with seed: “iginal text, homo natura; to bring it ab” iginal text, homo natura;… Continue reading Are RNN’s ready to replace journalists?
I’ve been doing Data Science projects, delivering software and doing Mathematical modelling for nearly 7 years (if you include grad school). I really don’t know everything, but these are a few things I’ve learned. Consider this like a ‘joel test‘ for Data Science. Use a reproducible framework like Cookiecutter Data Science. My workflow used to… Continue reading 3 tips for successful Data Science Projects
Quick note At the excellent PyData London conference, there was a lot of food for thought. One thing that came up was the concept of ‘data strategy’, there’s a lot of discussion about how to align, or write, or explain how data can help drive business transformation and be part of a business strategy. A… Continue reading Data Science Delivered
I spent about a year working as an Energy Analyst in Procurement at a large Telecommunications company. I’m by no means an expert but these are my own thoughts on where I feel ‘big data’ fits into procurement. Firstly for the stake of this argument let us consider procurement as a the purchase of goods… Continue reading Where does ‘Big Data’ fit into Procurement?
One question you have when you use Python is what do I do with my data. How do I process it and analyze it. The aim of this flow chart is to simply provide a simple to use ‘map’ of the PyData stack. At PyData Amsterdam I’ll present this and explain it in more detail… Continue reading A map of the PyData Stack